Within a cellular network, a power control process controls power of transmissions from a user equipment, such as a mobile handset, a cellular phone or the like, to e.g. a radio base station. Transmissions from the user equipment to the radio base station are referred to as uplink (UL) transmissions and transmissions from the radio base station to the user equipment are referred to as downlink (DL) transmissions. The radio base station can operate one or more cells.
A Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) network is a known cellular network. In the LTE network, one power control process is known as an UpLink Power Control (ULPC) process. The ULPC process is part of a so called Radio Resource Management (RRM) process of cellular networks, since the ULPC process directly impacts signal and interference levels at the radio base station. Moreover, battery consumption of the user equipment is impacted.
The ULPC process defined by the 3GPP for LTE network has several ULPC parameters, whose settings can be defined on a per cell basis. When the ULPC parameters are set to optimal, or near optimal values, low interference level and low battery consumption is achieved. However, at deployment of a new radio base station, it is difficult for an operator to find the optimal values for these parameters due to variable conditions relating to radio propagation, amount of traffic between the radio base station and the user equipment, interference and the like. For this reason, the ULPC parameters are often set to values, which are applied over the entire network. The values provide, in many cases, acceptable performance for the variable conditions. Thus, these values may be referred to as generic values herein. Even so, the generic values do not fully exploit flexibility of having the ULPC parameters defined on a per cell basis.
Therefore, after deployment, i.e. during operation, tuning of the ULPC parameters, i.e. adjustment towards the optimal, or near optimal values, may be performed by making various measurements in the network. The tuning is known as Coverage and Capacity Optimization, which is a feature of so called Self-Organizing Networks (SON). However, the provision of proper initial values, i.e. the standardized values, is greatly appreciated by the operator. Hence, the operator would benefit from a ULPC process having automated setting of the ULPC parameters.
The power control process itself has been studied in the literature. A number of different power control processes have been proposed. Among these, a fractional power control process has been selected by 3GPP for power control of a Physical Uplink Shared Channel (PUSCH) in LTE. The PUSCH is used for transmission of user data from the user equipment to the radio base station. In contrast thereto, a Physical Uplink Control Channel (PUCCH) is used for transmission of control data from the user equipment to the radio base station.
The fractional ULPC process comprises two working modes: an open-loop (OL) PC process and closed-loop (CL) PC process. The former aims to compensate for slow channel variations, while the latter adapts to changes in inter-cell interference conditions, measurement and/or power amplifier errors. In the standardized ULPC process, the user equipment transmits a specific transmission at a power is given by:
                                          P            TX                    =                      min            ⁢                                          {                                                      P                                          tx                      max                                                        ,                                                                                                                                          P                            0                                                    +                                                      α                            ·                            PL                                                                          ︸                                                                                                                                                        basic                              ⁢                                                                                                                          ⁢                              open                              ⁢                                                              -                                                            ⁢                              loop                                                                                                                                                                                          operating                              ⁢                                                                                                                          ⁢                              point                                                                                                                                            +                                                                                                                        Δ                            TF                                                    +                                                      f                            ⁢                                                          (                                                              Δ                                TPC                                                            )                                                                                                      ︸                                                                    dynamic                        ⁢                                                                                                  ⁢                        offset                                                              +                                                                                                                        10                            ·                                                          log                              10                                                                                ⁢                                                      M                            PUSCH                                                                          ︸                                                                    bandwidth                        ⁢                                                                                                  ⁢                        factor                                                                                            }                            ⁡                              [                dBm                ]                                                    ,                            (        1        )            
where Ptxmax is the maximum transmit power of the user equipment, P0 is a nominal PUSCH power, which has a resolution of 1 dB, α is a path-loss compensation factor, whose values can be 0, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1, PL are propagation losses, including antenna gains, ΔTF+f(ΔTPC) is a dynamic term depending on modulation scheme and power control commands and MPUSCH is the number of allocated Physical Resource Blocks (PRBs) for the specific transmission. Performance analysis of both OLPC and CLPC has been studied in many works, see e.g. Castellanos, C. U.; Villa, D. L.; Rosa, C.; Pedersen, K. I.; Calabrese, F. D.; Michaelsen, Per-Henrik; Michel, J., “Performance of Uplink Fractional Power Control in UTRAN LTE,” Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, vol., no., pp. 2517, 2521, 11-14 May 2008.
Now returning to the setting and/or tuning of the ULPC parameters. An aim of planning and tuning the ULPC parameters is to find the best tradeoff between network capacity and coverage. Such a tradeoff exists because increasing P0 and α in a cell (without adjusting them in surrounding cells) improves average and cell-edge user throughput in that cell, but degrades both average and cell-edge user throughput in surrounding cells. Because of this tradeoff, the ULPC tuning, when carried out for an entire network of an operator, can be formulated as a large-scale multivariate non-linear multi-objective optimization problem. This kind of problem is known to be hard to solve. Various solutions to do this problem do however exist. These solutions will be presented in the following, where P0 is the nominal PUSCH power and α is the path-loss compensation factor as above.
In Castellanos, C. U.; Villa, D. L.; Rosa, C.; Pedersen, K. I.; Calabrese, F. D.; Michaelsen, Per-Henrik; Michel, J., “Performance of Uplink Fractional Power Control in UTRAN LTE”, Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, vol., no., pp. 2517, 2521, 11-14 May 2008, a sensitivity analysis of ULPC parameters is carried out for different scenarios. Such an analysis evaluates network performance obtained by any pair of values of P0 and α over a network-level simulator implementing a regular scenario. Briefly, a regular scenario refers to that cells are spaced at equal distances from each other where the locations of the cells, i.e. the locations of the base stations, form a regular pattern. From this analysis, based on brute-force enumeration approach, two different pairs of values of P0 and α are obtained. One of the pairs is suggested for coverage-limited scenarios and the other pair is suggested for interference-limited scenarios. These suggested pairs can be used in the real network provided that the type of scenario is known a priori or it is deduced from network measurements, before or after deployment.
A similar approach is used in US20130310102, where cells are first classified based on pathloss measurements, and ULPC settings are then selected for each cell depending on the type of cell.
In S. Xu et al., “Coverage and capacity optimization in LTE network based on non-cooperative games”, The Journal of China Universities of Posts and Telecommunications, 2012, the problem of optimizing ULPC parameter settings in a multi-cell scenario is formulated as a non-cooperative game model. Then, a heuristic iterative optimization algorithm is proposed, where cells report ULPC settings to the network management system and exchange power and interference information with their neighbor cells. The proposed algorithm only adjusts P0 and does not consider closed-loop ULPC behavior.
Similarly, in M. Dirani et al., “Self-organizing networks in next generation radio access networks: Application to fractional power control”, Computer Networks, 2011, a self-tuning algorithm for adjusting the pathloss compensation factor, α, in the standardized fractional ULPC algorithm is proposed based on fuzzy reinforcement learning techniques.
In US2014051475, the ULPC optimization problem is solved by applying a genetic algorithm, where a simple network performance model is used to find the best solution in each new generation.
In K. Majewski, M. Koonert “Analytic uplink cell load approximation for planning fractional power control in LTE networks”, Telecommunication Systems (Springer), September 2011, the authors propose a self-planning algorithm to jointly optimize P0 and α in the fractional ULPC algorithm standardized for LTE. The algorithm consists of 3 stages. In a first stage, the best homogeneous, i.e., network-wide, P0 and α settings are found by a brute-force enumeration approach similar to that in “Performance of Uplink Fractional Power Control in UTRAN LTE” mentioned above.
Unlike “Performance of Uplink Fractional Power Control in UTRAN LTE” mentioned above, the method is applied to an irregular scenario by means of a system-level simulator implementing the real network scenario. In a second stage, a random local greedy search algorithm is used to find better P0 settings on a cell basis. In this algorithm, a cell is randomly selected and its P0 value is modified by a random amount, while keeping α fixed. This process continues until a large number of iterations are performed without improving the overall network performance. In the third stage, the same process is applied to find better α settings on a cell basis with the same P0 settings computed in the previous step.
The approach of classifying cells in terms of coverage and interference conditions used in “Performance of Uplink Fractional Power Control in UTRAN LTE” can only give a rough approximation of the optimal values of P0 and α, since the number of possible solutions is limited. Moreover, there still remains the issue of how those recommended settings are computed. For this purpose, a regular network scenario is assumed to reduce computational complexity and ease the interpretation of results. Thus, the results do not consider local irregularities in the scenario, which are the source of subtle differences between cells in the real network. All these limitations can also be applicable to the method in US20130310102, which is mentioned above.
The solution proposed in “Coverage and capacity optimization in LTE network based on non-cooperative games”, mentioned above, only deals with P0. Moreover, the method is conceived as a black-box approach, where the optimization algorithm is applied directly onto the real network, without any analytical treatment of the problem. Should the method be used for self-planning, a network model would still be needed, e.g., a network-level simulator. Thus, the execution time, or computational time, and the solution quality of the self-planning process would be dependent on the network model.
The general purpose optimization proposed in “Self-organizing networks in next generation radio access networks: Application to fractional power control” and US2014051475, which are mentioned above, require many iterations to converge to the optimal solution. This is a drawback if a solution that has a short computational time is needed. Moreover, the methods are also conceived as a black-box approach, where the optimization algorithm is applied directly onto the real network. If applied for self-planning, a network model would still be needed.
The solution in “Analytic uplink cell load approximation for planning fractional power control in LTE networks”, mentioned above, jointly optimizes P0 and α, and can be applied to irregular scenarios. However, it is based on a random search process, which requires evaluation of quality of many solutions, i.e. thousands of them. To assess the quality of each solution, computations must consider the whole network scenario, comprising many cells, which leads to a large execution time.
A problem of the known solutions may be that the computational complexity is too high. Accordingly, time for performing such calculations may also be too high. Thus, deployment of new base stations may have a high cost, e.g. in terms of computational time and required processing capacity.